Content
78%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is a lean, well-structured two-phase workflow with concrete commands, full prompt templates, and an explicit approval checkpoint between phases. Its main weakness is the absence of error-recovery feedback loops and a couple of minor actionability gaps in the fallback curl path.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is efficient with a compact inputs table and direct step/prompt blocks; only minor instances (the Trigger Keywords list and some Notes prose) could be trimmed, fitting the score-4 anchor of efficient with minor over-explanation. | 4 / 5 |
Actionability | It gives concrete commands and model IDs ("muapi image generate (model=nano-banana or wan2.5-text-to-image)", "muapi predict wait <request_id>") with full prompt templates, but the curl fallback uses a "{...}" body placeholder and exact CLI flag syntax is unspecified, leaving minor gaps. | 4 / 5 |
Workflow Clarity | Phase A and Phase B are clearly sequenced with an explicit approval checkpoint ("Present the baby image to the user for approval" / "Once the image is approved"), though no error-recovery feedback loop is provided for generation failures. | 4 / 5 |
Progressive Disclosure | The skill is under 50 lines with no need for external references, and content is organized into clear, navigable sections (Inputs, Steps, Trigger Keywords, Notes), meeting the simple-skill exception for a score of 5. | 5 / 5 |
Total | 17 / 20 Passed |